OpenRaiser/Intern-Atlas huggingface dataset:
Generating Media Background Checks for Automated Source Critical Reasoning
Venue: arXiv cs.CL 2024
Year: 2024

Not everything on the internet is true. This unfortunate fact requires both humans and models to perform complex reasoning about credibility when working with retrieved information. In NLP, this problem has seen little attention. Indeed, retrieval-augmented models are not typically expected to distrust retrieved documents. Human experts overcome the challenge by gathering signals about the context, reliability, and tendency of source documents - that is, they perform source criticism. We propose a novel NLP task focused on finding and summarising such signals. We introduce a new dataset of 6,709 "media background checks" derived from Media Bias / Fact Check, a volunteer-run website documenting media bias. We test open-source and closed-source LLM baselines with and without retrieval on this dataset, finding that retrieval greatly improves performance. We furthermore carry out human evaluation, demonstrating that 1) media background checks are helpful for humans, and 2) media background checks are helpful for retrieval-augmented models.
Zero-Shot Paragraph-level Handwriting Imitation with Latent Diffusion Models
Venue: arXiv cs.CV 2024
Year: 2024

The imitation of cursive handwriting is mainly limited to generating handwritten words or lines. Multiple synthetic outputs must be stitched together to create paragraphs or whole pages, whereby consistency and layout information are lost. To close this gap, we propose a method for imitating handwriting at the paragraph level that also works for unseen writing styles. Therefore, we introduce a modified latent diffusion model that enriches the encoder-decoder mechanism with specialized loss functions that explicitly preserve the style and content. We enhance the attention mechanism of the diffusion model with adaptive 2D positional encoding and the conditioning mechanism to work with two modalities simultaneously: a style image and the target text. This significantly improves the realism of the generated handwriting. Our approach sets a new benchmark in our comprehensive evaluation. It outperforms all existing imitation methods at both line and paragraph levels, considering combined style and content preservation.
Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details
Venue: arXiv cs.CV 2024
Year: 2024

Multi-center neuroimaging studies face technical variability due to batch differences across sites, which potentially hinders data aggregation and impacts study reliability.Recent efforts in neuroimaging harmonization have aimed to minimize these technical gaps and reduce technical variability across batches. While Generative Adversarial Networks (GAN) has been a prominent method for addressing image harmonization tasks, GAN-harmonized images suffer from artifacts or anatomical distortions. Given the advancements of denoising diffusion probabilistic model which produces high-fidelity images, we have assessed the efficacy of the diffusion model for neuroimaging harmonization. we have demonstrated the diffusion model's superior capability in harmonizing images from multiple domains, while GAN-based methods are limited to harmonizing images between two domains per model. Our experiments highlight that the learned domain invariant anatomical condition reinforces the model to accurately preserve the anatomical details while differentiating batch differences at each diffusion step. Our proposed method has been tested on two public neuroimaging dataset ADNI1 and ABIDE II, yielding harmonization results with consistent anatomy preservation and superior FID score compared to the GAN-based methods. We have conducted multiple analysis including extensive quantitative and qualitative evaluations against the baseline models, ablation study showcasing the benefits of the learned conditions, and improvements in the consistency of perivascular spaces (PVS) segmentation through harmonization.
Building FKG.in: a Knowledge Graph for Indian Food
Venue: arXiv cs.AI 2024
Year: 2024

This paper presents an ontology design along with knowledge engineering, and multilingual semantic reasoning techniques to build an automated system for assimilating culinary information for Indian food in the form of a knowledge graph. The main focus is on designing intelligent methods to derive ontology designs and capture all-encompassing knowledge about food, recipes, ingredients, cooking characteristics, and most importantly, nutrition, at scale. We present our ongoing work in this workshop paper, describe in some detail the relevant challenges in curating knowledge of Indian food, and propose our high-level ontology design. We also present a novel workflow that uses AI, LLM, and language technology to curate information from recipe blog sites in the public domain to build knowledge graphs for Indian food. The methods for knowledge curation proposed in this paper are generic and can be replicated for any domain. The design is application-agnostic and can be used for AI-driven smart analysis, building recommendation systems for Personalized Digital Health, and complementing the knowledge graph for Indian food with contextual information such as user information, food biochemistry, geographic information, agricultural information, etc.
The Design of an LLM-powered Unstructured Analytics System
Venue: arXiv cs.DB 2024
Year: 2024

LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and use cases that motivate its design. With Aryn, users specify queries in natural language and the system automatically determines a semantic plan and executes it to compute an answer from a large collection of unstructured documents. At the core of Aryn is Sycamore, a declarative document processing engine, that provides a reliable distributed abstraction called DocSets. Sycamore allows users to analyze, enrich, and transform complex documents at scale. Aryn includes Luna, a query planner that translates natural language queries to Sycamore scripts, and DocParse, which takes raw PDFs and document images, and converts them to DocSets for downstream processing. We show how these pieces come together to achieve better accuracy than RAG on analytics queries over real world reports from the National Transportation Safety Board (NTSB). Also, given current limitations of LLMs, we argue that an analytics system must provide explainability to be practical, and show how Aryn's user interface does this to help build trust.
Achieving Optimal Short-Blocklength Secrecy Rate Using Multi-Kernel PAC Codes for the Binary Erasure Wiretap Channel
Venue: arXiv cs.IT 2024
Year: 2024

We investigate practical short-blocklength coding for the semi-deterministic binary erasure wiretap channel (BE-WTC), where the main channel to the legitimate receiver is noiseless, and the eavesdropper's channel is a binary erasure channel (BEC). It is shown that under the average total variation distance secrecy metric, multi-kernel polarization-adjusted convolutional (MK-PAC) codes can achieve the best possible theoretical secrecy rate at blocklengths of 16, 32, 64, and 128 if the secrecy leakage is less than or equal to certain values.
Scaler: Efficient and Effective Cross Flow Analysis
Venue: arXiv cs.PF 2024
Year: 2024

Performance analysis is challenging as different components (e.g.,different libraries, and applications) of a complex system can interact with each other. However, few existing tools focus on understanding such interactions. To bridge this gap, we propose a novel analysis method "Cross Flow Analysis (XFA)" that monitors the interactions/flows across these components. We also built the Scaler profiler that provides a holistic view of the time spent on each component (e.g., library or application) and every API inside each component. This paper proposes multiple new techniques, such as Universal Shadow Table, and Relation-Aware Data Folding. These techniques enable Scaler to achieve low runtime overhead, low memory overhead, and high profiling accuracy. Based on our extensive experimental results, Scaler detects multiple unknown performance issues inside widely-used applications, and therefore will be a useful complement to existing work. The reproduction package including the source code, benchmarks, and evaluation scripts, can be found at https://doi.org/10.5281/zenodo.13336658.
Infiltrating the Sky: Data Delay and Overflow Attacks in Earth Observation Constellations
Venue: arXiv cs.NI 2024
Year: 2024

Low Earth Orbit (LEO) Earth Observation (EO) satellites have changed the way we monitor Earth. Acting like moving cameras, EO satellites are formed in constellations with different missions and priorities, and capture vast data that needs to be transmitted to the ground for processing. However, EO satellites have very limited downlink communication capability, limited by transmission bandwidth, number and location of ground stations, and small transmission windows due to high velocity satellite movement. To optimize resource utilization, EO constellations are expected to share communication spectrum and ground stations for maximum communication efficiency. In this paper, we investigate a new attack surface exposed by resource competition in EO constellations, targeting the delay or drop of Earth monitoring data using legitimate EO services. Specifically, an attacker can inject high-priority requests to temporarily preempt low-priority data transmission windows. Furthermore, we show that by utilizing predictable satellite dynamics, an attacker can intelligently target critical data from low-priority satellites, either delaying its delivery or irreversibly dropping the data. We formulate two attacks, the data delay attack and the data overflow attack, design algorithms to assist attackers in devising attack strategies, and analyze their feasibility or optimality in typical scenarios. We then conduct trace-driven simulations using real-world satellite images and orbit data to evaluate the success probability of launching these attacks under realistic satellite communication settings. We also discuss possible defenses against these attacks.
MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM
Venue: arXiv cs.CV 2024
Year: 2024

The Medical Segment Anything Model (MedSAM) has shown remarkable performance in medical image segmentation, drawing significant attention in the field. However, its sensitivity to varying prompt types and locations poses challenges. This paper addresses these challenges by focusing on the development of reliable prompts that enhance MedSAM's accuracy. We introduce MedSAM-U, an uncertainty-guided framework designed to automatically refine multi-prompt inputs for more reliable and precise medical image segmentation. Specifically, we first train a Multi-Prompt Adapter integrated with MedSAM, creating MPA-MedSAM, to adapt to diverse multi-prompt inputs. We then employ uncertainty-guided multi-prompt to effectively estimate the uncertainties associated with the prompts and their initial segmentation results. In particular, a novel uncertainty-guided prompts adaptation technique is then applied automatically to derive reliable prompts and their corresponding segmentation outcomes. We validate MedSAM-U using datasets from multiple modalities to train a universal image segmentation model. Compared to MedSAM, experimental results on five distinct modal datasets demonstrate that the proposed MedSAM-U achieves an average performance improvement of 1.7\% to 20.5\% across uncertainty-guided prompts.
A Comprehensive Analysis of the Future of Atomically Precise Manufacturing
Venue: arXiv cs.ET 2024
Year: 2024

Atomically Precise Manufacturing (APM) refers to the assembly of materials with atomic precision, representing a highly advanced technology with significant potential. However, the development of APM remains in its early stages, with applications largely confined to specialized fields and lacking cohesion within a unified discipline. The current literature on APM is often dominated by older, speculative papers that discuss its immense potential risks and benefits without sufficient grounding in the latest advancements or practical limitations that exist today. This paper aims to bridge this gap by providing a comprehensive assessment of current APM and near-APM technologies, as well as using the barriers to further progress to predict future developments. Through this analysis, we seek to establish a clearer understanding of the present state of the technology and then use these insights to predict the future trajectory of APM. By doing so, we aim to create a more grounded discourse on APM and its potential risks and benefits, while also guiding future research on the necessary regulations and safety considerations for this emerging field.
IVGF: The Fusion-Guided Infrared and Visible General Framework
Venue: arXiv cs.CV 2024
Year: 2024

Infrared and visible dual-modality tasks such as semantic segmentation and object detection can achieve robust performance even in extreme scenes by fusing complementary information. Most current methods design task-specific frameworks, which are limited in generalization across multiple tasks. In this paper, we propose a fusion-guided infrared and visible general framework, IVGF, which can be easily extended to many high-level vision tasks. Firstly, we adopt the SOTA infrared and visible foundation models to extract the general representations. Then, to enrich the semantics information of these general representations for high-level vision tasks, we design the feature enhancement module and token enhancement module for feature maps and tokens, respectively. Besides, the attention-guided fusion module is proposed for effectively fusing by exploring the complementary information of two modalities. Moreover, we also adopt the cutout&mix augmentation strategy to conduct the data augmentation, which further improves the ability of the model to mine the regional complementary between the two modalities. Extensive experiments show that the IVGF outperforms state-of-the-art dual-modality methods in the semantic segmentation and object detection tasks. The detailed ablation studies demonstrate the effectiveness of each module, and another experiment explores the anti-missing modality ability of the proposed method in the dual-modality semantic segmentation task.
Beyond ChatGPT: Enhancing Software Quality Assurance Tasks with Diverse LLMs and Validation Techniques
Venue: arXiv cs.SE 2024
Year: 2024

With the advancement of Large Language Models (LLMs), their application in Software Quality Assurance (SQA) has increased. However, the current focus of these applications is predominantly on ChatGPT. There remains a gap in understanding the performance of various LLMs in this critical domain. This paper aims to address this gap by conducting a comprehensive investigation into the capabilities of several LLMs across two SQA tasks: fault localization and vulnerability detection. We conducted comparative studies using GPT-3.5, GPT-4o, and four other publicly available LLMs (LLaMA-3-70B, LLaMA-3-8B, Gemma-7B, and Mixtral-8x7B), to evaluate their effectiveness in these tasks. Our findings reveal that several LLMs can outperform GPT-3.5 in both tasks. Additionally, even the lower-performing LLMs provided unique correct predictions, suggesting the potential of combining different LLMs' results to enhance overall performance. By implementing a voting mechanism to combine the LLMs' results, we achieved more than a 10% improvement over the GPT-3.5 in both tasks. Furthermore, we introduced a cross-validation approach to refine the LLM answer by validating one LLM answer against another using a validation prompt. This approach led to performance improvements of 16% in fault localization and 12% in vulnerability detection compared to the GPT-3.5, with a 4% improvement compared to the best-performed LLMs. Our analysis also indicates that the inclusion of explanations in the LLMs' results affects the effectiveness of the cross-validation technique.
Fitting trees to $\ell_1$-hyperbolic distances
Venue: arXiv cs.DS 2024
Year: 2024

Building trees to represent or to fit distances is a critical component of phylogenetic analysis, metric embeddings, approximation algorithms, geometric graph neural nets, and the analysis of hierarchical data. Much of the previous algorithmic work, however, has focused on generic metric spaces (i.e., those with no a priori constraints). Leveraging several ideas from the mathematical analysis of hyperbolic geometry and geometric group theory, we study the tree fitting problem as finding the relation between the hyperbolicity (ultrametricity) vector and the error of tree (ultrametric) embedding. That is, we define a vector of hyperbolicity (ultrametric) values over all triples of points and compare the $\ell_p$ norms of this vector with the $\ell_q$ norm of the distortion of the best tree fit to the distances. This formulation allows us to define the average hyperbolicity (ultrametricity) in terms of a normalized $\ell_1$ norm of the hyperbolicity vector. Furthermore, we can interpret the classical tree fitting result of Gromov as a $p = q = \infty$ result. We present an algorithm HCCRootedTreeFit such that the $\ell_1$ error of the output embedding is analytically bounded in terms of the $\ell_1$ norm of the hyperbolicity vector (i.e., $p = q = 1$) and that this result is tight. Furthermore, this algorithm has significantly different theoretical and empirical performance as compared to Gromov's result and related algorithms. Finally, we show using HCCRootedTreeFit and related tree fitting algorithms, that supposedly standard data sets for hierarchical data analysis and geometric graph neural networks have radically different tree fits than those of synthetic, truly tree-like data sets, suggesting that a much more refined analysis of these standard data sets is called for.
Multi-Modal Multi-Granularity Tokenizer for Chu Bamboo Slip Scripts
Venue: arXiv cs.CL 2024
Year: 2024

This study presents a multi-modal multi-granularity tokenizer specifically designed for analyzing ancient Chinese scripts, focusing on the Chu bamboo slip (CBS) script used during the Spring and Autumn and Warring States period (771-256 BCE) in Ancient China. Considering the complex hierarchical structure of ancient Chinese scripts, where a single character may be a combination of multiple sub-characters, our tokenizer first adopts character detection to locate character boundaries, and then conducts character recognition at both the character and sub-character levels. Moreover, to support the academic community, we have also assembled the first large-scale dataset of CBSs with over 100K annotated character image scans. On the part-of-speech tagging task built on our dataset, using our tokenizer gives a 5.5% relative improvement in F1-score compared to mainstream sub-word tokenizers. Our work not only aids in further investigations of the specific script but also has the potential to advance research on other forms of ancient Chinese scripts.
A dataset of Open Source Intelligence (OSINT) Tweets about the Russo-Ukrainian war
Venue: arXiv cs.SI 2024
Year: 2024

Open Source Intelligence (OSINT) refers to intelligence efforts based on freely available data. It has become a frequent topic of conversation on social media, where private users or networks can share their findings. Such data is highly valuable in conflicts, both for gaining a new understanding of the situation as well as for tracking the spread of misinformation. In this paper, we present a method for collecting such data as well as a novel OSINT dataset for the Russo-Ukrainian war drawn from Twitter between January 2022 and July 2023. It is based on an initial search of users posting OSINT and a subsequent snowballing approach to detect more. The final dataset contains almost 2 million Tweets posted by 1040 users. We also provide some first analyses and experiments on the data, and make suggestions for its future usage.
No Peer, no Cry: Network Application Fuzzing via Fault Injection
Venue: arXiv cs.CR 2024
Year: 2024

Network-facing applications are commonly exposed to all kinds of attacks, especially when connected to the internet. As a result, web servers like Nginx or client applications such as curl make every effort to secure and harden their code to rule out memory safety violations. One would expect this to include regular fuzz testing, as fuzzing has proven to be one of the most successful approaches to uncovering bugs in software. Yet, surprisingly little research has focused on fuzzing network applications. When studying the underlying reasons, we find that the interactive nature of communication, its statefulness, and the protection of exchanged messages render typical fuzzers ineffective. Attempts to replay recorded messages or modify them on the fly only work for specific targets and often lead to early termination of communication. In this paper, we discuss these challenges in detail, highlighting how the focus of existing work on protocol state space promises little relief. We propose a fundamentally different approach that relies on fault injection rather than modifying messages. Effectively, we force one of the communication peers into a weird state where its output no longer matches the expectations of the target peer, potentially uncovering bugs. Importantly, this weird peer can still properly encrypt/sign the protocol message, overcoming a fundamental challenge of current fuzzers. In effect, we leave the communication system intact but introduce small corruptions. Since we can turn either the server or the client into the weird peer, our approach is the first that can effectively test client-side network applications. Evaluating 16 targets, we show that Fuzztruction-Net outperforms other fuzzers in terms of coverage and bugs found. Overall, Fuzztruction-Net uncovered 23 new bugs in well-tested software, such as the web servers Nginx and Apache HTTPd and the OpenSSH client.
Multiagent Reinforcement Learning Enhanced Decision-making of Crew Agents During Floor Construction Process
Venue: arXiv cs.CE 2024
Year: 2024

Fine-grained simulation of floor construction processes is essential for supporting lean management and the integration of information technology. However, existing research does not adequately address the on-site decision-making of constructors in selecting tasks and determining their sequence within the entire construction process. Moreover, decision-making frameworks from computer science and robotics are not directly applicable to construction scenarios. To facilitate intelligent simulation in construction, this study introduces the Construction Markov Decision Process (CMDP). The primary contribution of this CMDP framework lies in its construction knowledge in decision, observation modifications and policy design, enabling agents to perceive the construction state and follow policy guidance to evaluate and reach various range of targets for optimizing the planning of construction activities. The CMDP is developed on the Unity platform, utilizing a two-stage training approach with the multi-agent proximal policy optimization algorithm. A case study demonstrates the effectiveness of this framework: the low-level policy successfully simulates the construction process in continuous space, facilitating policy testing and training focused on reducing conflicts and blockages among crews; and the high-level policy improving the spatio-temporal planning of construction activities, generating construction patterns in distinct phases, leading to the discovery of new construction insights.